Academic Hiring Networks and Institutional Prestige: A Case Study of Canadian Sociology
Bibliographic record
Abstract
This article examines the academic job market for Canadian sociology through its PhD exchange network. Using an original data set of employed faculty members in 2015 (N = 1,157), I map the hiring relationships between institutions and analyze the observed network structure. My findings show that institutional prestige is a likely organizing force within this network, reflective of a disproportionate number of faculty coming from a few centralized high-status institutions, as well as predominantly downward flows in hiring patterns. However, further investigation is needed to understand the role of prestige in Canadian higher education, which has been previously characterized as having a flat social structure. This requires attention toward the interrelationships between institutional prestige, scholarly competence, and department size situated within a segmented academic field in Canada. Overall, this study aims to encourage collective self-reflection and motivate discourse about status-based inequalities in our own discipline.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".